期刊
MOLECULES
卷 22, 期 9, 页码 -出版社
MDPI AG
DOI: 10.3390/molecules22091576
关键词
kinase selectivity profile; quantitative structure-activity relation; BCL:: CHEMINFO; artificial neural networks
资金
- U.S. Department of Veterans Affairs (Merit Reviews) [1I01BX002025-01]
- NIH [R01 GM099842, R01 DK097376]
- NSF [CHE 1305874]
The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure-activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model's performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23.
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